characteristic index
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2022 ◽  
Vol 14 (2) ◽  
pp. 782
Author(s):  
Baicang Guo ◽  
Qiang Hua ◽  
Lisheng Jin ◽  
Xianyi Xie ◽  
Zhen Huo ◽  
...  

Vehicle control requirements for longitudinal and lateral driver control are varied in different road geometries; this makes it irrational and superfluous to represent driving control characteristics with repetitive indices. To address this problem, the present study used multiple cross-analysis methods of vehicle running state parameters from experienced drivers in order to deeply study driving control characteristics in different road geometries. Six common road geometries with different driving control emphases were selected as typical road types and twenty-five experienced drivers were asked to perform an actual driving test. Taking the indices in the long straight road as the control variable, the indices in other roads were compared with it and judged according to the three methods: the overall distribution by box plots, significant difference test by analysis of variance (ANOVA) and relative distance calculation by technique for order preference by similarity to an ideal solution (TOPSIS). Moreover, the weight of the driving control characteristic index was calculated through the entropy weight method to reflect its importance. In this paper, the relationships between road geometry and driving control characteristics explicate the influence mechanism and interaction of road geometry on driving behavior, and the indicators that can reflect the control characteristics in different road types are obtained.


Author(s):  
Ganglei Li ◽  
Zhanxiong Wu ◽  
Jun Gu ◽  
Yu Zhu ◽  
Tiesong Zhang ◽  
...  

Metabolic signatures are frequently observed in cancer and are starting to be recognized as important regulators for tumor progression and therapy. Because metabolism genes are involved in tumor initiation and progression, little is known about the metabolic genomic profiles in low-grade glioma (LGG). Here, we applied bioinformatics analysis to determine the metabolic characteristics of patients with LGG from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). We also performed the ConsensusClusterPlus, the CIBERSORT algorithm, the Estimate software, the R package “GSVA,” and TIDE to comprehensively describe and compare the characteristic difference between three metabolic subtypes. The R package WGCNA helped us to identify co-expression modules with associated metabolic subtypes. We found that LGG patients were classified into three subtypes based on 113 metabolic characteristics. MC1 patients had poor prognoses and MC3 patients obtained longer survival times. The different metabolic subtypes had different metabolic and immune characteristics, and may have different response patterns to immunotherapy. Based on the metabolic subtype, different patterns were exhibited that reflected the characteristics of each subtype. We also identified eight potential genetic markers associated with the characteristic index of metabolic subtypes. In conclusion, a comprehensive understanding of metabolism associated characteristics and classifications may improve clinical outcomes for LGG.


2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wang Haohui ◽  
Sheng Xiaowei ◽  
Xu Yang

In recent years, the noise reduction research of the carpet tufting machine has been developing slowly. The research gaps of the existing work mainly focus on the noise source identification for the carpet tufting machine. MEEMD (EEMD) has been proposed to apply to source recognition on textile machinery. Due to the uniqueness of the MEEMD/EEMD, it is difficult to set suitable white noise control parameters. MEEMD (EEMD) has only been tested via simulation; however, it has not been mathematically proven or evaluated. This leads to inevitable flaws in the research conclusions, and even some conclusions are wrong. The contribution of this paper is twofold. First, in order to recognize the noise source of a carpet tufting machine, a method based on complete ensemble empirical mode decomposition (CEEMDAN) and Akaike information criterion (AIC) is proposed. The CEEMDAN-AIC method is applied to measure the noise signal of a carpet tufting machine and analyzed every single effective component selected. Noise source identification is realized by combining the vibration signal characteristics of the main parts of the carpet tufting machine. CEEMDAN is used to decompose the measured noise signal of the carpet tufting machine into a finite number of intrinsic mode functions (IMFs). Then, singular value decomposition (SVD) is performed on the covariance matrix of the IMF matrix to obtain the eigenvalue. Next, the number of effective IMFs is estimated based on the AIC criterion, and the effective IMFs are selected by combining the energy characteristic index and the Pearson correlation coefficient method. Furthermore, reconstruction and comparison of the decomposed signals of MEEMD and CEEMDAN proved that CEEMDAN is effective and accurate in source recognition. The results show that the noise signal of the carpet tufting machine is a mixture of multiple noise source signals. The main noise sources of the carpet tufting machine include shock caused by the impact of the tufted needle and looped hook and vibration of the hook-driven shaft and pressure plate. It provides theoretical support for the noise reduction of the carpet tufting machine.


2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Jianping Gao ◽  
Sijie Zhang ◽  
Yunyong He ◽  
Qi Zhang ◽  
Lu Sun ◽  
...  

A real-world driving experiment was performed in the Wen-Ma section of the G4217 Rong-Chang Freeway situated in the Sichuan Province to investigate the impact law of the pupil diameter of drivers in tunnel groups on the mountainous freeway. The eye-movement data of drivers were collected, and the percentage of pupil diameter variable (PPDV) was used as a visual characteristic index. The analysis of the overall change in the PPDV of drivers in the experimental sections demonstrated that the PPDV in tunnel groups differed significantly between the nontunnel sections and single tunnel sections. Subsequently, a related model for the PPDV of drivers and the length of the connecting zone between tunnels was established, its reliability evaluated, and the smooth mutation value obtained on the basis of the mutation theory. Thereafter, a tunnel group definition standard based on the visual effect of drivers was developed. A six-zone approach was devised for the analysis of tunnel groups, and the result revealed that the different zones in the tunnel group have different impact on PPDV of drivers. The results revealed that the different zones of tunnel group have different impact on PPDV of drivers. Furthermore, lighting transition facilities should be set in the exit section of tunnel. The PPDV of drivers was negatively correlated with the length of the connecting zone of tunnel groups, and 100 m is the recommended safety length threshold for the connecting zone of tunnel groups.


Polymers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2515
Author(s):  
Wei-Tai Huang ◽  
Chia-Lun Tsai ◽  
Wen-Hsien Ho ◽  
Jyh-Horng Chou

This study focuses on applying intelligent modeling methods to different injection molding process parameters, to analyze the influence of temperature distribution and warpage on the actual development of auto locks. It explores the auto locks using computer-aided engineering (CAE) simulation performance analysis and the optimization of process parameters by combining multiple quality characteristics (warpage and average temperature). In this experimental design, combinations were explored for each single objective optimization process parameter, using the Taguchi robust design process, with the L18 (21 × 37) orthogonal table. The control factors were injection time, material temperature, mold temperature, injection pressure, packing pressure, packing time, cooling liquid, and cooling temperature. The warpage and temperature distribution were analysed as performance indices. Then, signal-to-noise ratios (S/N ratios) were calculated. Gray correlation analysis, with normalization of the S/N ratio, was used to obtain the gray correlation coefficient, which was substituted into the fuzzy theory to obtain the multiple performance characteristic index. The maximum multiple performance characteristic index was used to find multiple quality characteristic-optimized process parameters. The optimal injection molding process parameters with single objective are a warpage of 0.783 mm and an average temperature of 235.23 °C. The optimal parameters with multi-objective are a warpage of 0.753 mm and an average temperature of 238.71 °C. The optimal parameters were then used to explore the different cooling designs (original cooling, square cooling, and conformal cooling), considering the effect of the plastics temperature distribution and warpage. The results showed that, based on the design of the different cooling systems, conformal cooling obtained an optimal warpage of 0.661 mm and a temperature of 237.62 °C. Furthermore, the conformal cooling system is smaller than the original cooling system; it reduces the warpage by 12.2%, and the average temperature by 0.46%.


Author(s):  
Wei-Tai Huang ◽  
Shih-Cheng Yang ◽  
Wen-Hsien Ho ◽  
Jinn-Tsong Tsai

Multiple performance objectives in turn-mill multitasking machining are investigated using the Taguchi method combined with the fuzzy theory. Using these two methods, optimized processing parameters can be rapidly identified to obtain optimized dimensional accuracy and geometrical shape angle, thus reducing machining cost and time. Herein, control factors for determining the single objective optimization parameter using the Taguchi robust process L9(34) orthogonal table were spindle speed (rpm), feed (mm/min), C-axis brake pressure (kg/cm2), axial cutting depth (mm), with dimensional accuracy and geometrical shape angle as objective characteristics. Then, signal-to-noise ratios of different groups were generated by gray correlation according to the experimental sequence to obtain the gray correlation coefficient for the calculation of the multiple performance characteristic index (MPCI). The MPCI results demonstrated that optimized dimensional accuracy was 0.005 mm and optimized geometrical shape angle was 0.004°. The optimized MPCI parameters were A3 (4000 rpm), B3 (250 mm/min), C3 (30 kg/cm2), and D3 (1.5 mm). It can reduce the processing for burr elimination and tool wear reduction by MPCI optimized process parameters.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yu-Ang Du

The car-free carrier platform is a product of the rapid development of the modern logistics industry and has a vital strategic value for promoting the construction of a country’s comprehensive transportation. However, due to the unreasonable platform pricing model, the industry is currently in a bottleneck period. In order to solve this problem, we established a gray correlation model to calculate the degree of correlation between each characteristic index and platform pricing based on the massive historical transaction data of a certain platform and performed K-means clustering on the results to discover the main factors affecting platform pricing. Based on the abovementioned results, we created a pricing optimization model based on the BP neural network, with the structure of 8-13-1 to predict the freight pricing of the order and test the prediction results. The test shows that the goodness of fit (R2) of the predicted value is close to 1, and the prediction error range is less than 3.7%, which proves the accuracy and effectiveness of the BP neural network model and provides an effective reference for the optimization of the pricing model of the car-free carrier platform.


2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110155
Author(s):  
Jin Gao ◽  
Fuquan Wu

The dynamic model of the front double wishbone suspension and the rear multi-link suspension of the vehicle are established. On the basis of detailed analysis of suspension kinematics, calculation method of wheel alignment angle and force calculation of suspension bushing, the influence mechanism of suspension bushing on the vehicle transient state is clarified, and the vehicle transient characteristic index is derived from the vehicle three-free dynamic model. The sensitivity analysis of the suspension bushing is carried out, and the bushing stiffness which has a great influence on the transient state of the vehicle is obtained. The bushing stiffness scale factor is used as the optimization variable, the vehicle transient characteristic index is used as the optimization target, and the NSGA-II optimization algorithm is used for multi-objective optimization. After optimization, one Pareto solution is selected to compare with the original vehicle, the comparison results show that the yaw rate gain, resonance frequency and delay time of yaw rate in the vehicle transient characteristic index are all improved, other optimization targets change less. In the steady-state comparison, the understeer tendency of the vehicle increases, and the roll angle of the vehicle increases but is within an acceptable range.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qiuping Li ◽  
Xing Zhang ◽  
Xin’an Wang ◽  
Tianxia Zhao ◽  
Changpei Qiu ◽  
...  

As traditional Chinese medicine (TCM) has gained more and more recognition in the world, Chinese medicine has also played its important role. However, traditional Chinese medicine equipment is relatively deficient, with insufficient functions and low degree of digitalization. For example, existing auscultation equipment can obtain few human characteristic indicators, which is difficult to meet the needs of reference in traditional Chinese medicine diagnosis. Based on this, this paper designed a human body characteristic index detection system based on the principle of traditional Chinese medicine, which includes respiratory and heartbeat signal acquisition device, meridian and acupoint signal acquisition device, temperature signal acquisition device, pulse and blood pressure acquisition device, processing module, keyword module, and output module. The respiratory and heartbeat signal acquisition device is used to collect the respiratory and heartbeat signal of human body. Meridian acupoint signal acquisition device is used to collect human meridian acupoint radio signals. The temperature signal acquisition device is used to collect the infrared temperature light wave signal of human body. Pulse and blood pressure acquisition devices are used to collect pulse and blood pressure signals. The processing module is used to obtain one or more human body characteristic indicators according to one or more of the respiration and heartbeat signals, meridians and acupoints signals, temperature signals, pulse, and blood pressure, including Qi and blood characteristic indicators, viscera and six meridian characteristic indicators, and temperature characteristic indicators. The keyword corresponding module is used to obtain the corresponding keyword representing the physiological state information of human body according to the one or more human body characteristic indicators. The output module is used to output the human body characteristic index and the key words. It includes the key words of Qi and blood state information, the key words of viscera state information, the key words of Qi and blood state information, etc. The system can be used for serious disease screening, chronic disease management, and risk early warning.


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